We consider each domain as a client for Digit5, Office-Caltech10 and DomainNet except that MNIST and Painting are held out as distillation data for
FedDF. We consider 4- and 8-client scenarios for CIFAR10 and consider a 4-client scenario for FMNIST. For these two datasets, 32000 training samples are held out as distillation data for FedDF. For FMNIST, CIFAR10, and Digit5, we randomly sample 2000 images for each client as the local training set. For Office-Caltech10 and DomainNet, 50% of the original data of each domain is used as the local training set. We use validation and test datasets on clients to report the best test accuracy over 5 different random seeds. We also leverage diversity loss from [1]} to improve the stability of the generator and adopt early stopping to avoid overfitting.
